4394results about "Picking devices" patented technology

The invention discloses an operation method of a humanoid agricultural robot based on path planning, which relates to the related technical field of agricultural robots and comprises the steps of multi-sensor data acquisition, data processing and map generation, map updating, target crop image recognition and positioning, global path planning, local path obstacle avoidance and picking action execution. The invention further discloses an operation system of the humanoid agricultural robot based on path planning. The operation system comprises a humanoid robot body, a visual sensor, a laser radar, a depth camera, an inertial measurement unit, an auxiliary sensor, an actuator module, a control system, a communication module and a power supply system. The inventor-shaped robot can recognize the positions and shapes of the tools in advance by using a sensitive visual perception system of the inventor-shaped robot, can skillfully cross over or bypass the tools by virtue of the flexibility of the inventor-shaped robot, can accurately determine the positions and distribution conditions of crops in a three-dimensional space, and generates a comprehensive picking environment map.

The invention provides a clamping force correction method and system based on an oil-tea tree branch clamping manipulator and a storage medium, and relates to the technical field of mechanization and automation, and the method comprises the steps of monitoring and analyzing oil-tea tree branches, an operation environment and a manipulator motion state, collecting key parameters, generating a clamping adaptability index and a clamping stability index, and correcting the clamping force of the oil-tea tree branches; the clamping adaptability index and the clamping stability index are combined, and a clamping control index is calculated; constructing a deep learning model, taking the historical key parameters as a training set, taking the corresponding historical manipulator clamping force as a label, training the model to obtain a clamping force prediction model, inputting the key parameters collected in real time into the model, outputting the predicted clamping force, combining the predicted clamping force with a clamping control index, and calculating the final clamping force. On the basis of obtaining key parameters in real time, the clamping adaptability index and the clamping stability index are generated, then the clamping control index is calculated, and the purpose of dynamically adjusting the clamping force is achieved.

A method for generating an agricultural cut-point for an agricultural item includes capturing an image of the agricultural item, generating a depth estimation of the agricultural item, segmenting the image of the agricultural item to generate a segmented image that identifies different segments of the agricultural item, detecting an agricultural feature of the agricultural item based on the image of the agricultural item, generating a two-dimensional cut-point based on the segmented image and the agricultural feature, and generating a three-dimensional cut-point based on the two-dimensional cut-point and the depth estimation of the agricultural item.

The invention provides high-altitude picking equipment. The high-altitude picking equipment comprises a walking vehicle, a mechanical arm and a picking and cutting device, the lower end of the mechanical arm is connected with the walking vehicle through a first rotating assembly, and the picking and cutting device is connected with the upper end of the mechanical arm; the picking and cutting device comprises an arc-shaped supporting body, an arc-shaped sliding rail, a hook claw mechanism, a sliding rail driving mechanism, a cutting mechanism and a hopper. A movable channel is formed in the arc-shaped supporting body, and the arc-shaped sliding rail is assembled in the movable channel in a sliding mode. The hook claw mechanism is arranged at one end of the arc-shaped supporting body, the sliding rail driving mechanism is arranged on the arc-shaped supporting body, and the sliding rail driving mechanism is in transmission connection with the arc-shaped sliding rail; the cutting mechanism is arranged on the arc-shaped sliding rail and located at the end away from the hook claw mechanism. The hopper is arranged on the outer side of the arc-shaped sliding rail, the hopper and the cutting mechanism are located at the same end of the arc-shaped sliding rail, and the hopper is located below the cutting mechanism. The oil palm fruit petiole cutting device has the advantages that petiole parts and petioles of fruit strings of oil palm fruits can be effectively, synchronously and rapidly cut, and the picking efficiency is improved.

The invention provides a method and a system for controlling a stable grasping force threshold value of a vibration manipulator for a camellia oleifera tree and the manipulator, and relates to the field of technical mechanical engineering and automatic control. The stable grasping force threshold value of the vibration manipulator for the camellia oleifera tree is obtained by obtaining a surface friction coefficient of the camellia oleifera tree, determining a damage threshold value, measuring a claw finger opening angle and clamping pressure when the manipulator works, and determining a clamping pressure range; the maximum pressure, the minimum pressure and the equivalent pressure are calculated, the clamping pressure of the mechanical arm is adjusted, the natural frequency and the maximum dynamic displacement response of the mechanical arm claw fingers are obtained, the claw finger opening angle and calculation data are adjusted based on the natural frequency and the maximum dynamic displacement response, and it is ensured that the grabbing force index is smaller than the damage threshold value through iterative adjustment. Mechanical simulation and actual measurement data are comprehensively utilized, through feedback adjustment and fine control, the problems of mechanical arm abrasion and low efficiency caused by excessive pressure and load in the prior art are successfully solved, the service life of the mechanical arm is prolonged, and the production efficiency of the mechanical arm is improved.

The invention provides a double-arm picking robot system and a control method, a robot body is mounted on a moving module through a rotary lifting waist and hip joint, and a storage module is arranged on the moving module; the moving module can drive the robot body and the storage module to move; a right multi-degree-of-freedom mechanical arm and a left multi-degree-of-freedom mechanical arm are arranged on the two sides of the robot body, a right arm tail end picking gripper is arranged at the right end of the right multi-degree-of-freedom mechanical arm, and a left arm tail end picking gripper is arranged at the tail end of the left multi-degree-of-freedom mechanical arm; an image acquisition module is arranged at the top of the robot body, a laser radar module is arranged on the moving module, and the image acquisition module and the laser radar module perform data transmission with the robot control module. Two arms are adopted for cooperative work, the structure of the humanoid mechanical arm is more suitable for the picking scene, and cooperation can be more effectively carried out.

The invention provides a fruit stem clamping and shearing agricultural robot and a picking control method thereof.The robot comprises a control cabinet, a mechanical arm and a visual navigation system which are installed on a crawler-type chassis supporting platform, and an upper computer control system and a lower computer control system are installed in the control cabinet; and the tail end of the mechanical arm is provided with a movable end effector with an identifying and positioning system and a light supplementing device. The movable end effector can flexibly adapt to picking requirements and complex working conditions of different crops; the upper computer control system is combined with a depth camera and a laser radar to collect data, and a YOLOv8-MC model is utilized to perform identification and key point positioning on a target crop in an image, so that the crop identification precision in a complex environment is remarkably improved, the probability of missing collection and mistaken collection is reduced, and the damage to fruits is reduced; when the upper computer control system constructs a global map, depth correction and map optimization are carried out on illumination changes, and the path planning and operation accuracy of the robot is further improved.

The invention provides an intelligent picking robot and a monitoring system. Field environment monitoring data are generated through an environment monitoring module; identifying one or more target picking objects based on a pre-trained picking object identification model through a picking object identification module according to the field environment monitoring data and the acquired image data of each to-be-picked object; generating a three-dimensional map of a to-be-picked area and an optimal picking path through a picking navigation module; a picking execution control module is used for controlling the walking structure to arrive at each target picking position in sequence, and controlling the mechanical arm structure to execute a picking task; therefore, the intelligent picking robot can adapt to picking tasks of fruits and vegetables of different types and different maturity degrees, accurate recognition, positioning and picking of a target picking object can be achieved in a complex agricultural environment, and the fruit and vegetable picking efficiency and quality are improved.

The invention belongs to the technical field of picking manipulators, and particularly discloses a fruit picking manipulator control method and a picking manipulator. Obtaining mechanical parameters of the fruits; according to the appearance parameters and the mechanical parameters, the clamping position and the clamping force of the picking manipulator on the fruits are calibrated; mechanical parameters between the fruit stems and the branches are obtained; and calibrating the moving track and the loading force of the picking manipulator according to the mechanical parameters between the fruit stems and the branches. According to the control method of the fruit picking manipulator, the clamping force can be set in advance before the fruits are picked, the damage rate of the fruits is reduced, and the picking efficiency is improved.

The invention discloses a picking robot, and relates to the technical field of robots, the picking robot comprises a moving trolley, an arch-shaped frame is mounted in the middle of the upper side of the moving trolley, the picking robot further comprises a picking steering mechanism, a bending fine adjustment mechanism, a visual camera and a picking mechanism, and the picking steering mechanism is mounted in the center of the top of the arch-shaped frame; the top of the picking steering mechanism is connected with a picking height adjusting mechanism, and the top of the picking height adjusting mechanism is connected with a swing control mechanism. The bending fine adjustment mechanism comprises a bending power assembly and a bending cylinder. The top of the swing control mechanism is connected with the bottom end of the bending cylinder through the bending power assembly. According to the picking robot, fruits are picked in the mode that the fruits are clamped and then rotated, the fruits are prevented from being damaged, the picked fruits fall down along the corrugated rubber pipe, the fruits are buffered and borne through the fruit falling anti-collision mechanism, and the situation that the fruits fall down along the corrugated rubber pipe too fast, and consequently the fruits are collided and damaged is avoided.

The invention provides a corridor closed harvesting automatic identification method and system, and relates to the technical field of harvesting, and the method comprises the steps: obtaining environmental parameters in a corridor closed region, calculating the growth condition of plants in a corridor, and obtaining the growth data of the plants; meanwhile, an image of a plant in the corridor is obtained in real time and segmented, a plant area image is obtained, the maturity of the plant is recognized, and plant maturity data is obtained; according to the plant growth data and the plant maturity data, whether the plants in the corridor meet the harvesting condition or not is judged, if the plants meet the harvesting condition, a harvesting execution mechanism is controlled to conduct harvesting operation on the plants in the corridor, harvesting amount data is obtained, and the commodity rate and the inventory are calculated based on the harvesting amount data; environmental parameters in the plant growth model are adjusted, according to the adjusted environmental parameters, the step of calculating the commodity rate and the inventory is repeatedly executed until the commodity rate reaches a preset commodity rate threshold value and the inventory is lower than a preset inventory threshold value, and corridor closed harvesting automatic identification is completed.

The invention provides a picking mechanical arm operation method and system based on visual feedback. The picking mechanical arm operation method comprises the steps that firstly, a multi-dimensional visual perception set including the shape contour, surface physical characteristic visual representation, connection part space distribution, growth environment dynamic interference and the like of a picked object is obtained; a mechanical arm collaborative decision-making model is constructed based on the multi-dimensional visual perception set, stress distribution characteristics of connection parts are analyzed, and optimal contact area parameters and mechanical arm posture collaborative parameters are determined; according to the parameters, generating a sub-time sequence action instruction set containing action information in the contact stage, the clamping stage and the separation stage; when the mechanical arm is controlled to execute the instruction set, a dynamic visual feedback set of the picking scene is obtained in real time; and finally, input parameters of the collaborative decision model are updated based on feedback, and instruction set parameters are adjusted, so that the mechanical arm completes picking operation on the premise of keeping the integrity of the picked object, and the intelligent level and the picking operation quality of the picking mechanical arm are improved.

Embodiments relate generally to a robotic fruit picking apparatus and a method of picking fruit. An example apparatus includes: a chassis; a robotic arm supported by the chassis and having an end effector, wherein the end effector includes a plurality of grippers and an extendable suction element; a vision system carried by the chassis and configured to identify pieces of fruit for picking; and a control system carried by the chassis and in communication with the vision system to control the robotic arm to pick identified pieces of fruit using the end effector. The control system may be configured to operate in a first mode to control the extendable suction element to extend the suction element towards a piece of fruit. The control system may be further configured to operate in a second mode following the first mode to retain contact with the piece of fruit by applying suction while freely allowing extension or retraction of the suction element based on movement of the piece of fruit.

A berry harvesting berries system including a mobile platform and a tendon-driven gripper having fingers made of a compliant material, which prevents unwanted damage to the berries.

The invention relates to the field of intelligent agricultural robots, in particular to a multi-robot collaborative harvesting method and system for woody oil plants based on swarm intelligence, and provides efficient harvesting of the woody oil plants by constructing a distributed sensing network and a semantic enhanced digital twinborn model, and the system adopts a three-layer decision architecture to formulate a harvesting strategy. The working efficiency is improved through self-adaptive task allocation and collaborative harvesting, multiple robots share experience through federated learning, group collaborative learning is achieved, harvesting performance is optimized, the reliability of the system is improved by 85%, single-point fault risks do not exist, the recognition accuracy is improved by 50% through a multi-modal sensing system, and the system is suitable for popularization and application. And an innovative harvesting solution is provided for the field of intelligent agricultural robots.

A method for managing an agricultural vehicle during harvesting process in a plantation of fruit trees, such as an orange orchard, the vehicle being shaped as a portal capable of moving over a crop row and provided with a couple of rotors (R) arranged to work simultaneously on both opposite sides of each plant of the crop row, the method including process of recognition of the trunks of the trees of a plantation to be worked by means of a first 3D sensor (3DS) system interfaced with processing means (CPU) of an agricultural vehicle (V), wherein the first 3D sensor (3DS) system includes a couple of 3D sensors arranged in a low portion of the agricultural vehicle at opposite sides of the vehicle oriented such that to converge in common point (P) circa on a vehicle center line axis (VC) in front of the vehicle, the process including fitting of pseudo-ellipsoids in a horizontal slice of a merged point cloud generated by the two 3D sensors, in order to identify trunks of the crop row.

The invention discloses a control method of a picking robot, and particularly relates to the technical field of intelligent control. According to the method, three-dimensional point cloud data of all fruits in a fruit cluster area are collected in real time, and a spatial connection topological structure of fruit stems is established; establishing a finite element mechanical analysis model based on the spatial connection topological structure of the carpopodium, analyzing a stress transmission path after the picking action acts on the carpopodium, and predicting position deviation of adjacent unpicked fruits; meanwhile, a multi-target path optimization algorithm is adopted to generate an initial path of continuous picking of the picking robot, and then the spatial position of the next fruit picking action is dynamically corrected according to fruit stem stress transmission path data; and abnormal change characteristics of fruit positions and postures are identified according to actual position deviation data of the picking action, so that control parameters and path planning of the next picking action are adjusted in real time. The precision and stability of picking path planning are improved, and the overall efficiency and reliability of picking operation of the robot are improved.

An atmospheric water generation system comprises water vapor consolidation systems configured to increase the relative humidity of a controlled air stream prior to condensing water from the controlled air stream. The water vapor consolidation system comprises a fluid-desiccant flow system configured to decrease the temperature of the desiccant to encourage water vapor to be absorbed by the desiccant from an atmospheric air flow. The desiccant flow is then heated to encourage water vapor evaporation from the desiccant flow into a controlled air stream that circulates within the system. The humidity of the controlled air stream is thereby increased above the relative humidity of the atmospheric air to facilitate condensation of the water vapor into usable liquid water.

The invention discloses a mechanical arm vibration auxiliary automatic sea-buckthorn harvesting device based on machine vision and a grabbing point positioning and sea-buckthorn harvesting method, relates to the technical field of sea-buckthorn harvesting, and solves the problem that sea-buckthorn harvesting is difficult. A crawler belt moving device is provided with a rubber crawler belt, a picking mechanical arm has six degrees of freedom, and the mechanical arm has six degrees of freedom; the tail end execution device is sent to a harvesting position in a point-to-point mode, the tail end execution device vibrates branches, close to the tail end, of a sea-buckthorn tree and detects the vibration amplitude of the branches of the sea-buckthorn tree, then comparison of a binocular vision system is combined, an excitation power source is combined to output an electric signal with the appropriate frequency, the tail end execution device has the optimal amplitude, and the recovery efficiency is improved; the collecting umbrella device works cooperatively and is unfolded when the tail end executing device works, fallen fruits are collected, the fallen fruits fall into a fruit frame of the fallen fruit collecting device, the fruit frame is placed on a pressure sensor, workers are informed to timely come to replace the fruit frame, and efficient and continuous harvesting work of the sea-buckthorn trees planted in a plantation in a large scale is achieved.

The invention relates to a double-arm intelligent collaborative tea picking method and device based on multi-strategy dynamic scheduling, and belongs to the field of robot automation. The method comprises the following steps: obtaining three-dimensional coordinate information of a target picking point; identifying and positioning potential branch and leaf shields, and generating space data of the shields; the method comprises the following steps: simultaneously projecting a shielding object and a clamping jaw geometric contour under an expected grabbing pose on a plane where a target picking point is located, analyzing the overlapping amount, the proximity and / or the density of the projections of the shielding object and the clamping jaw geometric contour, and generating a standardized shielding evaluation index; comparing the shielding evaluation index with a preset decision threshold, and if the shielding evaluation index is greater than the preset decision threshold, executing a master-slave collaborative picking method based on multi-stage positioning; otherwise, executing the parallel picking method based on dynamic collision-free region division. The problems that a traditional single-arm tea picking robot is low in operation efficiency, high in collision risk and insufficient in intelligent degree in a dense and unstructured tea tree environment are solved.

The invention relates to a solanaceous fruit picking robot and a fruit cluster observation pose estimation method and device.The solanaceous fruit picking robot comprises the fruit cluster observation pose estimation device, the device is used for achieving the fruit cluster observation pose estimation method, and the method comprises the following steps that RGB image data and depth data of solanaceous fruit plants are collected; respectively normalizing the RGB image data and the depth data, inputting the RGB image data and the depth data into the trained bimodal fusion semantic segmentation model, and predicting masks corresponding to two types of fruits and stalks in the RGB image; extracting depth information corresponding to a mask communication region, and carrying out local mask refinement in combination with a density clustering method; single fruit bunch point clouds are extracted in a clustering mode, point cloud center information of each cluster of fruit bunches is calculated, and stalk point clouds close to the fruit bunches are extracted for linear fitting; and determining the observation pose of the end camera according to the relative position of the point cloud center of the fruit bunch and the vertical point of the corresponding stem fitting straight line. The identification and positioning precision of the picking mechanical arm tail end camera on the fruit stem picking point is effectively improved.

Onboard and / or offboard crop stabilization for reducing movement within an agricultural environment to enhance robotic manipulation for crop harvesting.

The invention discloses a negative-pressure tea-leaf picker and a tea-leaf picking method. The negative-pressure tea-leaf picker comprises a frame body, the air duct is obliquely arranged on the frame body and is used for transporting the sheared tea leaves; the shearing tool is arranged at the lower end of one side, close to the advancing direction of the frame body, of the air duct; an adsorption opening for adsorbing tea leaves is formed in the lower end of the air duct; an air blowing opening is formed in the lower end of one side, far away from the advancing direction of the frame body, of the air duct and is connected with a fan through an air pipe; the air blowing opening comprises an adjusting plate arranged on the side close to the adsorption opening, the adjusting plate is rotationally arranged on the air channel, and the adsorption force at the adsorption opening is adjusted by adjusting the inclination angle between the adjusting plate and the air channel. According to the tea picking machine, the cut tea leaves are adsorbed and conveyed by forming negative pressure at the adsorption opening, the effectiveness of adsorbing and collecting the cut tea leaves is improved, and waste and loss caused by the fact that the tea leaves fall out of an air duct are prevented.

A factory mushroom picking robot includes a mobile platform, a chassis (100), a lifting device (200), a mushroom stick take-up device (300) and a mushroom picking device (400). The mushroom stick take-up device (300) is configured to take mushroom sticks out of a mushroom rack (4), and the mushroom sticks from different layers are taken through the lifting device (200). The mushroom picking device (400) includes a mechanical arm (402) and an execution end (403). The image data of the mushroom sticks are acquired through three depth cameras. Mushroom targets to be picked are identified through multi-view target matching and are divided in terms of the quality grade, so as to achieve graded picking. A vision-based graded picking method, a mushroom detection and grading method based on multi-view fusion and a non-transitory storage medium of executing corresponding method are further provided.

There is provided a system, installed on a vehicle or trailer, for performing an agricultural task. The system comprises a platform connecting to the vehicle or trailer and comprising a socket for an accessory. A plurality of accessories is provided, wherein one accessory is selected among them for installation in the accessory socket. A vision system is operatively connected to a computer and collects data of the environment about the accessory, the computer using the collected data to determine an instruction for performing the agricultural task and to send the instruction to the accessory installed in the accessory socket to perform the agricultural task. The accessory is interchangeable in the accessory socket depending on the agricultural task. The box handling stage and pallet mounting stage can be robotized for efficient automation.

The invention relates to the technical field of capsicum harvester control, and particularly discloses a control method, system and equipment based on a mountain small capsicum harvester, and the method comprises the steps: capturing the performance characteristic parameters of the mountain small capsicum harvester, and quantifying the terrain adaptation degree of the mountain small capsicum harvester; performance characteristic parameters of the small mountain pepper harvester can be automatically adjusted, visual feedback is provided for operation efficiency of the ready small mountain pepper harvester by continuously monitoring operation process performance parameters of the ready small mountain pepper harvester, follow-up data analysis is facilitated, and operation efficiency of the ready small mountain pepper harvester is improved. The operation process performance parameters of the ready mountain land small pepper harvester are controlled, the pepper harvesting quality parameters of the calibrated mountain land small pepper harvester are finally obtained, the harvesting completion quality index of the calibrated mountain land small pepper harvester is judged, and therefore a closed-loop feedback system is formed for the calibrated mountain land small pepper harvester. And the stability and the high efficiency of the small mountain pepper harvester in long-term use are ensured.

The invention provides a grape picking, collecting and managing device, relates to the field of grape management equipment, and aims to solve the problems that grapes are easily affected by harvesting equipment and damaged during harvesting at present, so that the grapes are inconvenient to store and the quality is reduced. A picking claw mechanism can be installed at the tail end of the picking mechanical arm, a shear mechanism and a temporary storage shell arranged on the picking claw mechanism can work cooperatively, fruit stems are sheared after grapes are supported, manual shearing operation is simulated to reduce damage to the grapes, meanwhile, the temporary storage shell can move to the position of the lifting platform, and the temporary storage shell can move to the lifting platform. The grapes are transferred into the storage box of the lifting platform after being opened from the bottom, the problem that the grapes are damaged due to transfer is solved, and the harvesting efficiency and quality are guaranteed.

Described herein are techniques for generating contextually rich plant images. A number of data captures of raw plant data are generated via a sensing unit configured to navigate a growing facility. Metadata is generated and assigned to the raw plant data including at least one of: plant location, timestamp, plant identification, plant strain, facility identification, facility location, facility type, health risk factors, plant conditions, and human observations. Images generated by the sensing unit are analyzed and pixel annotations are generated in the images based on their relation to one or more plant well-being features. Data tags are generated and assigned the data captures based on an analysis of the data captures. The data tags are text phrases linking a particular data capture to a specific threat to plant well-being.

The invention discloses an intelligent litchi picking and sorting machine and a working method thereof. The intelligent litchi picking and sorting machine comprises a rack, a walking mechanism, a picking mechanism and a sorting mechanism, the walking mechanism, the picking mechanism and the sorting mechanism are arranged on the rack, and the picking mechanism and the sorting mechanism are both located above the walking mechanism; the picking mechanism comprises a manipulator, a picking device and a picking camera, the picking device and the picking camera are both arranged at the tail end of the manipulator, the picking device comprises an electric mechanical claw, and the electric mechanical claw is used for grabbing fruits recognized by the picking camera; the sorting mechanism comprises a mechanical arm, a shearing device and a sorting camera, the shearing device and the sorting camera are both arranged at the tail end of the mechanical arm, after the electric mechanical claw grabs the fruits, the sorting camera recognizes branches connected with the fruits, and the shearing device shears the branches connected with the fruits. By adopting the arrangement, automatic picking and sorting of litchis can be realized, so that the picking efficiency is improved, and meanwhile, the quality and the yield of picked fruit products can be ensured.

The invention provides a picking robot carrying a multi-modal generation type large model and a picking method. The picking robot comprises a moving device, a control device, a six-axis mechanical arm, a tail end picking executing mechanism, a tail end camera, a side camera, a global camera and sensors. The control device is loaded with a software program, and the software program is loaded with the applied multi-modal generative large model. According to the picking robot carrying the multi-modal generation type large model and the picking method, the RGB image in the real world is directly mapped into the action of the picking robot, so that the picking robot can learn from visual input and simulate and execute a specific action, and when a task of specific picking operation is executed, the picking operation is more accurate, and the picking efficiency is improved. Data acquired from a field environment are imported into the large model in real time, and according to real-time feedback, the action state is adaptively adjusted in the process of executing picking operation according to a planned path, so that the picking accuracy is improved.